The Pathfinder dataset is composed of \(900,000\)  training and \(100,000\)  test images, and has two classes.
We use the standard cross-entropy loss with L2 regularization.
In our experiment, in addition to the first convolution, CycleNet has a single cycle at \(128\times 128 \times 128\)  features, global pooling (as in [1]}) and a dense layer.
The experiments run on a single GPU (GeForce RTX 2080 Ti), Adam optimizer was used with learning rate \(0.001\) .
We use 5 times more distractors than the base dataset.
